1. Field of the Invention
The present invention relates to the field of fabricating semiconductor devices, and, in particular, to process control techniques for manufacturing processes, wherein an improved process control quality is achieved by detecting process failures on the basis of process data.
2. Description of the Related Art
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This holds especially true in the field of semiconductor fabrication, since, here, it is essential to combine cutting edge technology with mass production techniques. It is, therefore, the goal of semiconductor manufacturers to reduce the consumption of raw materials and consumables while at the same time improve product quality and process tool utilization. The latter aspect is especially important since, in modern semiconductor facilities, equipment is required which is extremely cost-intensive and represents the dominant part of the total production costs. For example, in manufacturing modern integrated circuits, 500 or more individual processes may be necessary to complete the integrated circuit, wherein failure in a single process step may result in a loss of the complete integrated circuit. This problem is even exacerbated in that the size of substrates, on which a plurality of such integrated circuits are processed, steadily increases, so that failure in a single process step may entail the loss of a large number of products.
Therefore, the various manufacturing stages have to be thoroughly monitored to avoid undue waste of manpower, tool operation time and raw materials. Ideally, the effect of each individual process step on each substrate would be detected by measurement and the substrate under consideration would be released for further processing only if the required specifications were met. A corresponding process control, however, is not practical, since measuring the effects of certain processes may require relatively long measurement times, frequently ex situ, or may even necessitate the destruction of the sample. Moreover, immense effort, in terms of time and equipment, would have to be made on the metrology side to provide the required measurement results. Additionally, utilization of the process tool would be minimized since the tool would be released only after the provision of the measurement result and its assessment.
The introduction of statistical methods, also referred to as statistical process control (SPC), for adjusting process parameters significantly relaxes the above problem and allows a moderate utilization of the process tools while attaining a relatively high product yield. Statistical process control is based on the monitoring of the process output to thereby identify an out-of-control situation, wherein a causality relationship may be established to an external disturbance. After occurrence of an out-of-control situation, operator interaction is usually required to manipulate a process parameter so as to return to an in-control situation, wherein the causality relationship may be helpful in selecting an appropriate control action. Nevertheless, in total, a large number of dummy substrates or pilot substrates may be necessary to adjust process parameters of respective process tools, wherein tolerable parameter drifts during the process have to be taken into consideration when designing a process sequence, since such parameter drifts may remain undetected over a long time period or may not be efficiently compensated for by SPC techniques.
Recently, a process control strategy has been introduced and is continuously being improved allowing a high degree of process control, desirably on a run-to-run basis, with a moderate amount of a measurement data. In this control strategy, so-called advanced process control (APC), a model of a process or of a group of interrelated processes is established and implemented in an appropriately configured process controller. The process controller also receives information including pre-process measurement data and/or post-process measurement data, as well as information related, for instance, to the substrate history, such as type of process or processes, the product type, the process tool or process tools in which the products are to be processed or have been processed in previous steps, the process recipe to be used, i.e., a set of required sub-steps for the process or processes under consideration, wherein possibly fixed process parameters and variable process parameters may be contained, and the like. From this information and the process model, the process controller determines a controller state or process state that describes the effect of the process or processes under consideration on the specific product, thereby permitting the establishment of an appropriate parameter setting of the variable parameters of the specified process recipe to be performed with the substrate under consideration.
Thus, the APC controller may have a predictive behavior, which is typically referred to as model predictive control (MPC). Model predictive control schemes, although originally used for real-time control of continuous processes, may also be used for run-to-run control situations in that the continuous time parameter is replaced by a discrete process run index, wherein the controller is now configured to respond to substantially continuous disturbances, also referred to as process drifts, and to substantially step-wise disturbances, which may be considered as process shifts. Thus, run-to-run control may provide the potential of compensating for predictable, that is deterministic, disturbances such as process shifts and drifts.
Even though APC strategies may significantly contribute to yield improvement and/or enhanced device performance and/or a reduction on production costs, nevertheless a statistical probability exists that even process outputs obtained by using an APC technique may be outside of predefined value ranges, thereby resulting in yield loss. In high-volume production lines, even short delays between the occurrence of an out-of-control situation, indicating for instance an equipment failure, and its detection may lead to substantial monetary losses. Consequently, it may be advantageous to apply fault detection and classification (FDC) techniques in combination with other control strategies, such as APC and/or SPC.
For instance, one important application of run-to-run control is the monitoring of lithography processes, as the lithography process is one of the most critical processes during the fabrication of semiconductor devices. Moreover, the lithography process may typically provide enhanced control capabilities as the process is typically performed step-wise for each individual substrate, that is, a plurality of individual imaging steps are usually performed for each substrate, thereby enabling individual control of each single step. Consequently, across-wafer uniformity may be controlled by appropriately adapting process parameters of the individual imaging steps. In addition, the lithography has a somewhat unique position in that the process output of the lithography process may be assessed and the lithography process may be repeated when specific process margins are not achieved. On the other hand, lithography is a highly cost-intensive process and undue reprocessing of out-of-control substrates may substantially contribute to overall production costs. One critical aspect in the lithography process is, in addition to the appropriate alignment of the reticle pattern with respect to the wafer, the adjustment of the appropriate depth of focus, since the range for the available focus depth is related to the exposure wavelength and the numerical aperture, wherein, for a given numerical aperture, a reduced exposure wavelength leads to a reduced depth of focus. Thus, with ever decreasing feature sizes in modem integrated circuits, calling for shorter exposure wavelengths, the probability for grossly defocused exposure fields, which may be referred to as “hot spots,” increases, thereby resulting in significant line width variations on the respective chips. However, standard inspection and overlay measurement techniques may not efficiently detect such hot spot errors, thereby significantly contributing to yield loss, since non-detected hot spot errors may prevent a corresponding wafer from being reworked and may also delay the detection of any failure mechanisms in the corresponding exposure tool or any other process tool related to the imaging process.
In view of the situation described above, there exists a need for an enhanced technique that enables a control strategy for a lithography process in which one or more of the problems identified above may be avoided or the effects thereof at least be significantly reduced.